article · Alexandria Engineering Journal
This research introduces a new implementation of the Artificial Ecosystem Optimizer (AEO) technique for optimising electrical distribution networks. The AEO, inspired by ecosystem energy transfer mechanisms, is used to determine the best placement of distributed generators (DGs) and capacitors, alongside reconfiguring the power distribution system. The technique was tested on a practical 59-bus Cairo distribution system under various loading conditions. The AEO's performance was compared against several other optimisation algorithms, including Jellyfish Search Optimizer and Particle Swarm Optimization. The results showed that the AEO significantly outperformed the other methods in terms of achieving better, more consistent, and more robust optimisation outcomes. Specifically, it led to substantial reductions in power losses across different loading levels.
Optimising electrical distribution networks is crucial for improving efficiency and reliability. Reducing power losses translates into energy savings, lower operational costs, and a more stable power supply for consumers. This research offers a more effective computational tool for achieving these vital improvements in power system management.
This research presents an applied optimisation algorithm that could be used by utility companies and power system operators to enhance the efficiency of existing electrical distribution networks. The technique, demonstrated on a practical case study, could inform software tools for network planning and operation, leading to reduced power losses and improved grid performance. It appears to be at an applied research stage, with potential for integration into commercial grid management systems.
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In this article, a new implementation of Artificial Ecosystem Optimizer (AEO) technique is developed for distributed generators (DGs) and capacitors allocation considering the Reconfiguration of Power Distribution Systems (RPDS). The AEO is inspired from three energy transfer mechanisms involving production, consumption, and decomposition in an ecosystem. In the production mechanism, the production operator allows AEO to produce a new individual randomly, whereas the search space exploration can be improved as illustrated in the consumption mechanism and exploitation can be performed in the decomposition. A practical case study of 59-bus Cairo distribution system in Egypt is simulated with different loading percentages. For optimizing the performance of that practical network, the AEO algorithm is employed for different scenarios. Besides, the results obtained by recent optimization techniques which are Jellyfish Search Optimizer (JFS), Supply Demand Optimizer (SDO), Crow Search Optimizer (CSO), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO) and Whale Optimization Algorithm (WOA) are compared with the developed AEO. The simulation results demonstrate the efficacies and superiority of the AEO compared to the others. It surpasses the other algorithms in terms of obtaining the best, mean, worst, and standard deviations. After optimal RPDS and DGs placements, the power losses are decreased by 78.4, 77.84 and 71.4% at low, nominal and high levels, respectively. However, the best scenario with its application prospects is mentioned after optimal RPDS, DGs, and capacitors where the power losses are decreased by 68.8, 85.87 and 89.91% at low, nominal and high levels, respectively.
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DOI: 10.1016/j.aej.2021.11.035
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